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Improving the Effectiveness of Image Classification Structural Methods by Compressing the Description According to the information Content Criterion

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Computers, Materials & Continua 2024年第8期80卷 3085-3106页

作者： Yousef Ibrahim Daradkeh Volodymyr Gorokhovatskyi Iryna Tvoroshenko Medien Zeghid Department of Computer Engineering and information College of Engineering inWadi AlddawasirPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Informatics Kharkiv National University of Radio ElectronicsKharkiv61166Ukraine electronics and Micro-electronics Laboratory Faculty of SciencesUniversity of MonastirMonastir5000Tunisia

The research aims to improve the performance of image recognition methods based on a description in the form of a set of keypoint *** main focus is on increasing the speed of establishing the relevance of object and etalon descriptions while maintaining the required level of classification *** class to be recognized is represented by an infinite set of images obtained from the etalon by applying arbitrary geometric *** is proposed to reduce the descriptions for the etalon database by selecting the most significant descriptor components according to the information content *** informativeness of an etalon descriptor is estimated by the difference of the closest distances to its own and other *** developed method determines the relevance of the full description of the recognized object with the reduced description of the *** practical models of the classifier with different options for establishing the correspondence between object descriptors and etalons are *** results of the experimental modeling of the proposed methods for a database including images of museum jewelry are *** test sample is formed as a set of images from the etalon database and out of the database with the application of geometric transformations of scale and rotation in the field of *** practical problems of determining the threshold for the number of votes,based on which a classification decision is made,have been *** has revealed the practical possibility of tenfold reducing descriptions with full preservation of classification *** the descriptions by twenty times in the experiment leads to slightly decreased *** speed of the analysis increases in proportion to the degree of *** use of reduction by the informativeness criterion confirmed the possibility of obtaining the most significant subset of features for classification,which guarantees a decent

关键词： Description reduction description relevance descriptor image classification information content keypoint processing speed

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2D all-organic perovskites refine flexible electronics

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Science China Materials 2024年第7期67卷 2365-2366页

作者： Wei Zhou Bing Chen college of Electronic and Optical Engineering and college of Flexible electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China

Recent years have witnessed a paradigm shift with the advent of perovskites,a class of materials with a distinct CaTiO_(3)-type crystalline structure that show remarkable promise across a wide array of applications,including photovoltaics,photodetectors,lighting,lasers,and so *** them,the development of two-dimensional(2D)perovskites has garnered significant attention due to their tunable structures(mainly Dion-Jacobson and Ruddlesden-Popper structures)and enhanced stability in comparison with their 3D counterparts[1].Amidst the burgeoning research on perovskites,on the other side,all-organic perovskites stand out as a particularly intriguing subset[2].Unlike traditional perovskites that typically comprise all-inorganic(e.g.,CsPbBr_(3))or organic-inorganic hybrid components(e.g.,(PEA)_(2)PbI_(4)),all-organic perovskites are composed entirely of carbon-or nitrogen-based molecules.

关键词： perovskite inorganic entirely

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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Computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Road map for, and technical challenges of, carbon-nanotube integrated circuit technology

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National Science Review 2024年第3期11卷 14-16页

作者： Jia Si Panpan Zhang Zhiyong Zhang Key Laboratory for the Physics and Chemistry of Nanodevices and Center for Carbon-based electronics School of Electronics Peking University State Key Laboratory of information Photonics and Optical Communications Beijing University of Posts and Telecommunications

The past decade has witnessed an explosion of research on carbon-nanotube(CNT) electronics due to their outstanding properties, including an ultrathin body, a high carrier mobility and a high saturation velocity.

关键词： TRANSISTORS

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An angular blinking jamming method based on electronically controlled corner reflectors

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Journal of Systems Engineering and electronics 2024年第2期35卷 330-338页

作者： GAN Lin WU Zehao WANG Xuesong LI Jianbing State Key Laboratory of Complex Electromagnetic Environmental Effects on electronics and information System National University of Defense TechnologyChangsha 410073China

Passive jamming is believed to have very good potential in countermeasure *** this paper,a passive angular blinking jamming method based on electronically controlled corner reflectors is *** amplitude of the incident wave can be modulated by switching the corner reflector between the penetration state and the reflection state,and the ensemble of multiple corner reflectors with towing rope can result in complex angle decoying *** of the decoying effect on corner reflectors’radar cross section and positions are analyzed and *** show that the angle measured by a monopulse radar can be significantly interfered by this method while the automatic tracking is employed.

关键词： monopulse radar angular blinking jamming corner reflector amplitude-comparison angle measurement metasurface

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Fast compressed sensing spectral measurement with adaptive gradient multiscale resolution

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Chinese Physics B 2024年第2期33卷 298-304页

作者：蓝若明刘雪峰李天平白成杰 School of Physics and electronics Shandong Normal UniversityJinan 250014China Key Laboratory of electronics and information Technology for Space Systems National Space Science CenterChinese Academy of SciencesBeijing 100190China

We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed *** method can apply variable measurement resolution over the entire spectral range to reduce the measurement time by over 75%compared to a global high-resolution *** the characteristics of the human retina system,the resolution distribution follows the principle of gradually *** system allows the spectral peaks of interest to be captured dynamically or to be specified a priori by a *** system was tested by measuring single and dual spectral peaks,and the results of spectral peaks are consistent with those of global high-resolution measurements.

关键词： spectrometer compressed sensing adaptive gradient multiscale resolution fast measurement

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Efficient unequal error protection for online fountain codes

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Journal of Systems Engineering and electronics 2024年第2期35卷 286-293页

作者： SHI Pengcheng WANG Zhenyong LI Dezhi LYU Haibo School of electronics and information Engineering Harbin Institute of TechnologyHarbin 150001China Shenzhen Academy of Aerospace Technology Shenzhen 518057China

In this paper,an efficient unequal error protection(UEP)scheme for online fountain codes is *** the buildup phase,the traversing-selection strategy is proposed to select the most important symbols(MIS).Then,in the completion phase,the weighted-selection strategy is applied to provide low *** performance of the proposed scheme is analyzed and compared with the existing UEP online fountain *** results show that in terms of MIS and the least important symbols(LIS),when the bit error ratio is 10-4,the proposed scheme can achieve 85%and 31.58%overhead reduction,respectively.

关键词： online fountain code random graph unequal error protection(UEP) rateless code

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Lead-free perovskites-based photonic synaptic devices with zero electric energy consumption

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Science China(information Sciences) 2024年第6期67卷 433-441页

作者： Dandan HAO Di YANG Haixia LIANG Jia HUANG Fukai SHAN college of electronics and information Qingdao University Key Laboratory of Biofuels Qingdao Institute of Bioenergy and Bioprocess TechnologyChinese Academy of Sciences School of Materials Science and Engineering Tongji University National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University

The von Neumann bottleneck is a critical limitation in synaptic devices. Therefore, artificial synaptic devices resembling biological neuromorphic synapses have been developed to overcome the von Neumann bottleneck. However, synaptic devices require voltages, which results in considerable energy consumption. Here, photonic synaptic devices with the vertical structure of indium tin oxide（ITO）/SnO2/Al2O3/CsBi3I10/Au are fabricated, which can work in the self-powered mode owing to the photovoltaic effect endowed by a vertical multilayer structure. Several fundamental synaptic functions, such as excitatory postsynaptic current, paired-pulse facilitation, short-term plasticity（STP）, long-term plasticity（LTP）, pulse-frequency-dependent plasticity, transition of STP to LTP, and the learning experience are emulated. Moreover, Morse-coded external light information is decoded by self-powered photonic synaptic devices. The results indicate that self-powered photonic synaptic devices based on lead-free perovskites exhibit great potential for efficient neuromorphic computing and optical wireless communication.

关键词： photonic synaptic devices lead-free perovskites zero energy consumption synaptic plasticity Morse code

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ResNeSt-biGRU: An Intrusion Detection Model Based on Internet of Things

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Computers, Materials & Continua 2024年第4期79卷 1005-1023页

作者： Yan Xiang Daofeng Li Xinyi Meng Chengfeng Dong Guanglin Qin School of Computer and electronics information Guangxi UniversityNanning530004China Guangxi colleges and Universities Key Laboratory of Multimedia Communications and information Processing Guangxi UniversityNanning530004China

The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasingdemands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device hascaught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. Thishas resulted in a myriad of security challenges, including information leakage, malware propagation, and financialloss, among others. Consequently, developing an intrusion detection system to identify both active and potentialintrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practicalintrusion detection model that combines the strengths of ResNeSt, a variant of Residual Neural Network, andbidirectionalGated RecurrentUnitNetwork (biGRU).Our ResNeSt-biGRUframework diverges fromconventionalintrusion detection systems (IDS) by employing this dual-layeredmechanism that exploits the temporal continuityand spatial feature within network data streams, a methodological innovation that enhances detection *** conjunction with this, we introduce the PreIoT dataset, a compilation of prevalent IoT network behaviors, totrain and evaluate IDSmodels with a focus on identifying potential intrusion traffics. The effectiveness of proposedscheme is demonstrated through testing, wherein it achieved an average accuracy of 99.90% on theN-BaIoT datasetas well as on the PreIoT dataset and 94.45% on UNSW-NB15 dataset. The outcomes of this research reveal thepotential of ResNeSt-biGRU to bolster security measures, diminish intrusion-related vulnerabilities, and preservethe overall security of IoT ecosystems.

关键词： Internet of Things cyberattack intrusion detection internet security

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Sepsis Prediction Using CNNBDLSTM and Temporal Derivatives Feature Extraction in the IoT Medical Environment

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Computers, Materials & Continua 2024年第4期79卷 1157-1185页

作者： Sapiah Sakri Shakila Basheer Zuhaira Muhammad Zain Nurul Halimatul Asmak Ismail Dua’Abdellatef Nassar Manal Abdullah Alohali Mais Ayman Alharaki Department of information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Kingdom of Saudi Arabia Department of Computer Science and information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityRiyadh11671Kingdom of Saudi Arabia

Background:Sepsis,a potentially fatal inflammatory disease triggered by infection,carries significant healthimplications *** detection is crucial as sepsis can rapidly escalate if left *** in deep learning(DL)offer powerful tools to address this ***:Thus,this study proposeda hybrid CNNBDLSTM,a combination of a convolutional neural network(CNN)with a bi-directional long shorttermmemory(BDLSTM)model to predict sepsis *** the proposed model provides a robustframework that capitalizes on the complementary strengths of both architectures,resulting in more accurate andtimelier ***:The sepsis prediction method proposed here utilizes temporal feature extraction todelineate six distinct time frames before the onset of *** time frames adhere to the sepsis-3 standardrequirement,which incorporates 12-h observation windows preceding sepsis *** models were trained usingthe Medical information Mart for Intensive Care III(MIMIC-III)dataset,which sourced 61,522 patients with 40clinical variables obtained from the IoT medical *** confusion matrix,the area under the receiveroperating characteristic curve(AUCROC)curve,the accuracy,the precision,the F1-score,and the recall weredeployed to evaluate ***:The CNNBDLSTMmodel demonstrated superior performance comparedto the benchmark and other models,achieving an AUCROC of 99.74%and an accuracy of 99.15%one hour beforesepsis *** results indicate that the CNNBDLSTM model is highly effective in predicting sepsis onset,particularly within a close proximity of one ***:The results could assist practitioners in increasingthe potential survival of the patient one hour before sepsis onset.

关键词： Temporal derivatives hybrid deep learning predicting sepsis onset MIMIC III machine learning(ML) deep learning

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